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Building Data-Driven Applications with LlamaIndex

Building Data-Driven Applications with LlamaIndex

By : Andrei Gheorghiu
5 (10)
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Building Data-Driven Applications with LlamaIndex

Building Data-Driven Applications with LlamaIndex

5 (10)
By: Andrei Gheorghiu

Overview of this book

Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.
Table of Contents (18 chapters)
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1
Part 1:Introduction to Generative AI and LlamaIndex
4
Part 2: Starting Your First LlamaIndex Project
8
Part 3: Retrieving and Working with Indexed Data
12
Part 4: Customization, Prompt Engineering, and Final Words

Summary

In this chapter, we covered a quick introduction to GenAI and LLMs. You learned how LLMs such as GPT work and some of their capabilities and limitations. A key takeaway is that while powerful, LLMs have weaknesses – such as the potential for false information and lack of reasoning – that require mitigation techniques. We discussed RAG as one method to overcome some LLM limitations.

These lessons provide useful background on how to approach LLMs practically while being aware of their risks. At the same time, you learned the importance of techniques such as RAG to address LLMs’ potential downsides.

With this introductory foundation in place, we are now ready to dive into the next chapter where we will explore the LlamaIndex ecosystem. LlamaIndex offers an effective RAG framework to augment LLMs with indexed data for more accurate, logical outputs. Learning to leverage LlamaIndex tools will be the natural next step to harness the power of LLMs in a proficient...

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